cs.AREmpirical
This paper presents NELSSA, a serving system that integrates GPUs with Processing-near-Memory (PNM) devices to efficiently handle mixed-length workloads in LLMs, achieving up to 5.5x decode throughput improvement and 15x P99 latency reduction.
Proposes an integrated GPU-PNM serving system for handling mixed-length workloads in LLMs, improving decode throughput and reducing latency.
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Modern LLMs and their agentic applications are broadening the range of serving workloads, spanning context lengths from a few hundred tokens to hundreds of thousands. As these requests frequently interleave within the same serving window, LLM serving systems must handle highly heterogeneous mixed-length workloads. Such mixed-length workloads expose fundamental inefficiencies in GPU-centric serving architectures, whose throughput depends on large, memory-constrained batches. In this paper, we present NELSSA, an LLM serving system that integrates GPUs with real-world Processing-near-Memory (PNM) accelerator devices to efficiently support mixed-length workloads. NELSSA employs length-based request placement to route short-context requests to GPUs and long-context requests to the PNM tier, incorporating runtime migration to accommodate dynamic context growth without recomputation. We prototype NELSSA as an end-to-end system, implementing device-level sparse attention on PNM, GPU decode kernels, and a host-side runtime that orchestrates scheduling and cross-tier memory movement over a CXL-enabled infrastructure with RPC and RDMA support. Across mixed-length LLM workloads, NELSSA improves decode throughput by up to 5.5x in tokens/sec and reduces P99 latency by up to 15x compared to GPU-only baselines. Our end-to-end prototype and experimental results suggest that integrated GPU-PNM serving, enabled by CXL-based disaggregation, is a promising system paradigm for scalable and flexible LLM infrastructures that support evolving workloads.